7 Popular LLMs Explained in 7 Minutes

AI is now a core part of our digital experience, powering everything from Google Search and Gmail to virtual assistants and content creation tools. At the heart of these systems lie Large Language Models (LLMs). But with new models emerging frequently, it’s difficult to keep track of which model excels at what, be it reasoning, coding, multilingual processing, or multimodal interaction.

The complexity only grows. For example, GPT-4o is capable of processing text, images, voice, and video, but DeepSeek is trained on 670 billion parameters and only uses around 37 billion per task, which is very efficient. More than 100 new LLMs were launched in 2024 alone across the globe, and according to a recent Deloitte survey, 60% of AI users have no idea about the technologies they use every day. This lack of understanding hinders innovation and stops users from making smart decisions.

To make things easier, we've developed a fast, 7-minute primer that explains the 7 best LLMs today: BERT, GPT-4o, LLaMA 4, PaLM 2, Gemini 2.5, Mistral, and DeepSeek. We'll dissect their architectures, abilities, and distinguishing features so you can quickly grasp how each model functions and which best fits your application. If you're a developer, researcher, or simply interested in AI, this is your way around the long articles and jargon-heavy explanations.

7 popular LLMs:

Large Language Models (LLMs) have restructured the field of artificial intelligence by enabling machines to understand, generate, and interact using human language. Below are 7 of the most influential LLMs shaping today's AI aspect.

1. BERT (Bidirectional Encoder Representations from Transformers)

1. Developed by Google in 2018.
2. Uses an encoder-only architecture
3. Reads text both left-to-right and right-to-left
4. Trained using Masked Language Modeling (MLM) and Next Sentence Prediction (NSP).
5. Excels in understanding text, not generating it.
6. Strong in question answering, text classification, and NER tasks.
7. Open-source and widely used in NLP pipelines.

2. GPT (Generative Pretrained Transformer)

1. Created by OpenAI
2. Uses a decoder-only architecture
3. Trained in an autoregressive manner (predicts next word)
4. GPT-4o is multimodal and understands text, image, audio, and video
5. Great for text generation, creative writing, and few-shot learning
6. Closed-source, accessible via API only
7. Highly capable but limited by its proprietary license

3. LLaMA (Large Language Model Meta AI)

1. Developed by Meta AI
2. Uses a decoder-only architecture
3. Comes in multiple sizes: 7B to 70B+ parameters
4. Incorporates RoPE, SwiGLU, and RMSNorm for performance
5. Open weights are available for research use only
6. Efficient and powerful for local experimentation
7. Licensing restricts commercial use

4. PaLM (Pathways Language Model)

1. Created by Google Research
2. Based on a decoder-only architecture
3. Original model had 540B parameters
4. PaLM 2 is smaller, faster, and multilingual
5. Supports code generation, translation, and reasoning
6. Powers Google tools like Bard and Duet AI
7. Proprietary and limited to Google products

5. Gemini

1. Google’s most advanced multimodal LLM
2. Uses Mixture of Experts (MoE) architecture
3. Can handle 1 million+ tokens in a single input (long context)
4. Versions include Gemini Flash (fast) and Gemini Pro (full-scale)
5. Designed for language, vision, audio, and video tasks
6. Closed-source and integrated into Google apps
7. Efficient and scalable, but not open to public use

6. Mistral

1. A new player in the LLM space
2. Offers both decoder-only and Mixture of Experts models
3. Mistral 7B is lightweight but powerful
4. Mixtral (8x7B) activates only 2 experts at a time
5. Supports reasoning, code generation, and faster inference
6. Some models are open-source, others are not
7. Well-balanced between performance and openness

7. DeepSeek

1. Developed in China
2. Uses Sparse MoE architecture
3. Trained on 670B parameters, with only 37B active during inference
4. Highly efficient and reasoning-focused
5. Performs well in multilingual tasks and NLP reasoning
6. Open-source and gaining popularity in Asia
7. Less known globally but very promising

Final Words

Rapid growth of AI capabilities in understanding, generation, and multimodal reasoning is witnessed in the evolution of Large Language Models such as BERT, GPT, LLaMA, PaLM, Gemini, Mistral, and DeepSeek. Each model possesses distinct strengths, such as improved reasoning ability, open research accessibility, or multimodal capability, that render them crucial tools for current AI applications and dictate the future of intelligent systems.

Kunal Singh Saurabh Anand